Financial markets have experienced several negative sigma events in recent years; these eventsoccur with much more regularity than current risk models can predict. There is no guarantee thatthe training set's data generating process will be the same in the test set in finance. Mathematicalmodels...

We explore in this paper the use of deep signature models to predict equity financial time series returns. First, we use signature transformations to model the underlying shape of the input equity returns; further assuming the underlying shape remains the same, we predict future values based on...

We examine in this paper a critical question in finance: the use of large nonlinear over-parametrized models or simpler models to forecast financial time series and the balance between underfitting and overfitting, the bias-variance trade-off, and the absolute performance in the test set. The...

We examine in this paper the training and test set performance of several equity factor models with a dataset of 20 years of data, 1,200 stocks and 100 factors. First, we examine several models to forecast expected returns, which can be used as baselines for more complex models: linear...

A generative model is a statistical model of the joint probability distribution. We built a generative model for univariate time series in finance using a Variational Autoencoder (VAE) neural network architecture. We test the model in SP500 and the Heston Model widely used for option pricing and...

We use a reinforcement model to compute the hedging policy for Credit Valuation Adjustment ( CVA ) problems. Reinforcement learning can be used to solve financial applications ofintertemporal choice. In finance, common problems of this kind include pricing and hedging ofcontingent claims,...